The present invention relates to a pattern recognition technology utilizing a computer. More particularly, this invention relates to a pattern recognition apparatus and a pattern recognition method which can be applied to a method of, and an apparatus for, executing pattern recognition suitable for an automated urinary sediment examination system for classifying particles in urine, and which can accurately classify objects having great individual differences without being affected by the individual differences.
A urinary sediment examination is the one that examines solid components such as blood cells, tissue cells, etc., contained in urine and reports the kinds and amounts of the components. It has been customary for a laboratory expert to first centrifuge a predetermined amount of urine, then to dye the resulting sediment components, to sample them on a preparation and to microscopically observe the components. Each component is classified in accordance with its features such as the shape, dyeability, and so forth. A method of imaging the solid components in urine as a still image is described as a technology for automatically executing the urinary sediment examination in, for example, JP-A-57-500995, JP-A63-94156 and JP-A-5-296915. These technologies involve the steps of passing a sample through a flow passage (flow cell) having a specific shape, causing particles in the sample to flow through a broad imaging area, turning on a flash lamp when any solid components are detected in the sample and imaging a magnified image of the solid components in urine as a still image. The sediment components imaged as the still image in this way are automatically classified by separating the region of the sediment components from a background region on the image, determining image feature parameters in the sediment component region and classifying the sediment components on the basis of these feature parameters. An area, a perimeter, a mean color density, etc., are used as the image feature parameters. JP-A-1-119765, for example, describes a region dividing method of a blood cell image as one of the technologies of separating the region of the solid components from the background region on the image. This reference segments the region of the image in a color space by using a threshold value determined from a density histogram. JP-B-58-29872 and JP-A-3-131756, for example, describe the classification of the blood cell images as a technology of classifying objects from the image feature parameters. JP-B-58-29872 employs a discrimination logic or dicision tree constituted by statistical discriminating functions in multiple stages on the basis of the image feature parameters. JP-A-3-131756 employs a hierarchical network as a recognition logic.
The individual difference of each specimen is great in the urinary sediment examination and even those objects which ought to be classified into the same class exhibit great differences in the shape and dyeability from specimen to specimen. Therefore, the individual difference renders a great problem for automatic classification. For instance, the size and the shape of the blood cells in urine vary with pH of urine, its specific gravity and osmotic pressure. Because the white blood cell is generally greater in size than the read blood cell, it is rare that they are wrongly classified. However, there may be the case where the white blood cell of a specimen shrinks depending on the condition such as the pH, the specific gravity, the osmotic pressure, etc., and is wrongly classified as the red blood cell. When this classification is done with eye, all the specimens are first checked as a whole so as to sort out typical those objects which can be judged reliably as the white blood cell and then to judge the overall tendency of the specimens that the white blood cells are rather small as a whole, or that there are a large number of white blood cells which are deformed, for example. Thereafter, the objects which cannot be classified easily are tackled. Even though the white blood cells are so small that they are likely to be mistaken as the red blood cells, for example, they are classified as the white blood cells if the typical blood cells of the specimen are small as a whole and if the red blood cells do not appear. Therefore, the conventional pattern recognition method which decides the classification class to which a given pattern belongs from only the given pattern cannot eliminate the influences of the individual difference for each specimen, and is not free from the problem that classification accuracy drops for those specimens in which rather small white blood cells peculiarly appear. It is an object of the present invention to provide pattern recognition which reduces wrong classification of objects resulting from the individual difference in pattern recognition of those objects which exhibit different features depending on the individuals (samples) even though the objects ought to be classified into the same class.
The first construction of a pattern recognition apparatus according to the present invention comprises first pattern recognition means for inputting a set of input samples constituted by a plurality of input patterns, classifying each input pattern in the set of input samples into a classification class to which this input pattern belongs, evaluating reliability of this classification result and outputting the classification class to which this input pattern belongs, as being recognizable, for the input pattern for which a classification result having high reliability can be obtained; first storage means for storing those input patterns among the set of input samples which are evaluated as having low reliability of the classification result obtained by the first pattern recognition means and for which recognition is suspended; second storage means for storing those input patterns among the set of input samples which are evaluated as having high reliability of the classification result obtained by the first pattern recognition means and which are judged as recognizable, and for storing the classification class, to which the input patterns belong, outputted by the first pattern recognition means; second pattern recognition means being constructed by using the input pattern stored in the second memory means and the classification class to which the input pattern belongs as a training sample, for inputting the input pattern stored in the first storage means and outputting the classification class to which the input pattern belongs; and pattern recognition method construction means for constructing the second pattern recognition means by using the input pattern stored in the second storage means and the classification class to which this input pattern belongs, as a training sample.
In the first construction described above, the second construction of the pattern recognition apparatus according to the present invention includes reference pattern recognition method storage means, and wherein the second pattern recognition means is initialized by using the content of the reference pattern recognition method storage means before the pattern recognition method construction means constructs the second pattern recognition method.
The third construction of the pattern recognition apparatus according to the present invention comprises pattern recognition means being set to the initial state before one set of input samples comprising a plurality of input patterns are inputted, for classifying each input pattern in the set of input samples to a classification class to which each input pattern belongs, evaluating reliability of this classification result and outputting a classification class to which the input pattern belongs, as being recognizable, for those input patterns for which a classification result having high reliability is obtained; first storage means for storing the input patterns in the set of samples for which the classification result is evaluated as having low reliability and for which recognition is suspended by the pattern recognition means; second storage means for storing those recognizable input patterns in the set of samples which are evaluated as having high reliability of the classification result obtained from the pattern recognition means and for storing the classification class, to which the input pattern belongs, outputted from the pattern recognition means; pattern recognition method adjustment means for optimizing the pattern recognition means by using the input pattern and the classification class to which the input pattern belongs, stored in the second storage means, as a training sample; and reference pattern recognition method storage means for storing the initial state of the pattern recognition means; wherein the input pattern stored in the first storage means is inputted to the pattern recognition means after the pattern recognition means is optimized by the pattern recognition method adjustment means, and the classification class to which the input pattern belongs is outputted.
The fourth construction of the pattern recognition apparatus according to the present invention comprises input pattern storage means for storing one set of input samples comprising a plurality of input patterns; a neural network for executing pattern recognition; reliability evaluation means for evaluating reliability of a pattern recognition result for the pattern inputted by the output value of the neural network; initial weight value storage means for storing the number of layers of the neural network in the initial state, the number of neurons in each layer and weight value between the neurons; neural network training means for training the neural network; and pattern recognition result storage means; wherein a plurality of patterns constituting one set of input samples are stored in the pattern storage means, and after the neural network is initialized in accordance with the content of the initial weight value storage means, the following processes (1) to (4) are executed for all the input patterns stored in the pattern storage means and are repeated until reliability of the pattern recognition by the neural network for all the patterns is evaluated as high, or until a predetermined number of times is reached, and then the pattern recognition result stored in the pattern recognition result storage means is outputted:
(1) A process for executing pattern recognition by inputting one by one the patterns stored in the pattern storage means to the neural network;
(2) A process for storing the recognition result in the pattern recognition result storage means;
(3) A process for evaluating reliability of the pattern recognition result for the input pattern by the reliability evaluation means by using the output value of the neural network; and
(4) A process for executing training of the neural network by the neural network training means by using the recognition result for those input patterns which are evaluated as having high reliability.
The fifth construction of the pattern recognition apparatus according to the present invention comprises pattern recognition means for inputting one set of input samples comprising a plurality of input patterns, classifying each input pattern in the set of input samples to a classification class to which the input pattern belongs, evaluating reliability of the classification result, outputting the classification class to which the input pattern belongs, as being recognizable for those of the input patterns for which the classification result having high reliability can be obtained, suspending recognition for those of input patterns which have low reliability of the classification result, and outputting a first applicant and a second applicant of the classification class to which the input pattern is to be classified; storage means for storing the first and second applicants of the classification class to which those input patterns in the set of samples which are evaluated as having low reliability of the classification result obtained from the pattern recognition means and for which recognition is suspended are to be classified; a counter for counting, for each classification class, the number of those objects which are evaluated as having high reliability of the classification result and as being recognizable, from the pattern recognition means, and for which the classification class is outputted; and reasoning means for deciding the classification class of the input pattern by using the content of the counter and the first and second applicants of the classification class for those input patterns which are evaluated as having low reliability of the classification result, which are stored in the storage means and for which recognition is suspended.
In a pattern recognition apparatus for urinary sediment examination, for example, the present invention first classifies only those objects which provide a classification result having high reliability for each urine sample, then builds afresh a pattern recognition logic reflecting the tendency of the objects in the sample such as the tendency that the number of white blood cells which are rather small in size is large as a whole, and classifies those objects for which the classification result having high reliability cannot be obtained, by using this recognition logic. To build the pattern recognition logic reflecting the tendency of the objects, pattern recognition is first made once, reliability of the recognition result so obtained is evaluated, and the pattern recognition logic is built once again by using the objects having high reliability of the recognition result and their classification result as the training sample. The present invention re-builds the recognition logic by using those objects which can be accurately classified by the apparatus, and can automatically optimize the recognition logic without human judgement.
In the case of the urinary sediment examination, in particular, the object of all the classification items seldom appear in one specimen, and a rule that the objects of specific classification items appear depending on the kind of diseases and troubles is generally determined. When a certain object which cannot be classified easily into which of the red blood cell or the white blood cell appears, which of other objects appear is first examined, and when a large number of typical white blood cells are observed but no red blood cell at all can be observed, the object has a high possibility of the white blood cell. It is known empirically that there is a statistical correlation among the number of appearances of various kinds of objects. When a cast appears, for example, the possibility of the trouble of a renal tubule is high and the possibility of appearance of the renal tubular epithelial cell is high in the sediment. Therefore, when the object for which it is difficult to judge whether the renal tubular epithelial cell or the white blood cell appears, there is a rule to judge that the object has a high possibility of the renal tubular epithelial cell unless the cast appears separately and the typical white blood cell exists. When re-classification is conducted by such a rule for those objects for which the recognition result having high reliability cannot be obtained by pattern recognition, classification accuracy can be improved.